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Model Building for Semiparametric Mixtures [article]

Ramani S. Pilla, Francesco Bartolucci, Bruce G. Lindsay
2006 arXiv   pre-print
., 1985; Lesperance and Kalbfleisch, 1992; Roeder, 1994; Lindsay, 1995; McLachlan and Peel, 2001; Pilla and Loader, 2003; Scott, 2004a; Pilla and Charnigo, 2005) .  ...  Similar behavior of the C-EM algorithm, in reaching a sub-optimal solution, was observed by Pilla and Lindsay (2001) for the galaxy data.  ... 
arXiv:math/0606077v1 fatcat:cc2ajxbrafgofpxuuyzoruqmje

A smart electric bike for smart cities [article]

Shaun Sweeney, Robert Shorten, David Timoney, Giovanni Russo, Francesco Pilla
2022 arXiv   pre-print
This is a Masters Thesis completed at University College Dublin, Ireland in 2017 which involved augmenting an off-the-shelf electric bike with sensors to enable new services to be delivered to cyclists in cities. The application of primary interest was to control the cyclist's ventilation rate based on the concentration of local air pollutants. Detailed modelling and system design is presented for our Cyberphysical system which consisted of a modified BTwin e-bike, Cycle Analyst sensors, the
more » ... list themselves, a Bluetooth connected smartphone and our algorithms. Control algorithms to regulate the proportion of power the cyclist provided as a proxy for their ventilation rate were proposed and validated in a basic way, which were later proven significantly further in Further Work (see IEEE Transactions on Intelligent Transportation Systems paper: The basic idea was to provide more electrical assistance to cyclists in areas of high air pollution to reduce the cyclist ventilation rate and thereby the amount of air pollutants inhaled. This presents an interesting control challenge due to the human-in-the-loop characteristics and the potential for impactful real life applications. A background literature review is provided on energy as it relates to cycling and some other applications are also discussed. A link to a video which demonstrates the system is provided, and also to a blog published by IBM Research about the system.
arXiv:2203.06679v1 fatcat:ua4rde352bdpblzsecjgu6d6yi

Spatially distributed potential evapotranspiration modeling and climate projections

Salem Gharbia, Trevor Smullen, Laurence Gill, Paul Johnston, Francesco Pilla
2018 Zenodo  
Evapotranspiration integrates energy and mass transfer between the Earth's surface and atmosphere and is the most active mechanism linking the atmosphere, hydrosphsophere, lithosphere and biosphere. This study focuses on the fine resolution modeling and projection of spatially distributed potential evapotranspiration on the large catchment scale as response to climate change. Six potential evapotranspiration designed algorithms, systematically selected based on a structured criteria and data
more » ... ilability, have been applied and then validated to long-term mean monthly data for the Shannon River catchment with a 50 m2 cell size. The best validated algorithm was therefore applied to evaluate the possible effect of future climate change on potential evapotranspiration rates. Spatially distributed potential evapotranspiration projections have been modeled based on climate change projections from multi-GCM ensembles for three future time intervals (2020, 2050 and 2080) using a range of different Representative Concentration Pathways producing four scenarios for each time interval. Finally, seasonal results have been compared to baseline results to evaluate the impact of climate change on the potential evapotranspiration and therefor on the catchment dynamical water balance. The results present evidence that the modeled climate change scenarios would have a significant impact on the future potential evapotranspiration rates. All the simulated scenarios predicted an increase in potential evapotranspiration for each modeled future time interval, which would significantly affect the dynamical catchment water balance. This study addresses the gap in the literature of using GIS-based algorithms to model fine-scale spatially distributed potential evapotranspiration on the large catchment systems based on climatological observations and simulations in different climatological zones. Providing fine-scale potential evapotranspiration data is very crucial to assess the dynamical catchment water balance to setup managem [...]
doi:10.5281/zenodo.3878829 fatcat:g7tzoiiabnbmxfmujmevsckh5m

Tales of a City: Sentiment Analysis of Urban Green Space in Dublin [article]

Mohammadhossein Ghahramani, Nadina Galle, Carlo Ratti, Francesco Pilla
2021 arXiv   pre-print
Social media services such as TripAdvisor and Foursquare can provide opportunities for users to exchange their opinions about urban green space (UGS). Visitors can exchange their experiences with parks, woods, and wetlands in social communities via social networks. In this work, we implement a unified topic modeling approach to reveal UGS characteristics. Leveraging Artificial Intelligence techniques for opinion mining using the mentioned platforms (e.g., TripAdvisor and Foursquare) reviews is
more » ... novel application to UGS quality assessments. We show how specific characteristics of different green spaces can be explored by using a tailor-optimized sentiment analysis model. Such an application can support local authorities and stakeholders in understanding--and justification for--future urban green space investments.
arXiv:2107.06041v1 fatcat:pyb6s27luzd5jmlsdbebddvsku

Analysis of Carbon Dioxide Emissions from Road Transport Using Taxi Trips

Mohammadhossein Ghahramani, Francesco Pilla
2021 IEEE Access  
FRANCESCO PILLA is currently an Associate Professor of smart cities with UCD, Ireland.  ... 
doi:10.1109/access.2021.3096279 fatcat:6pq6yx6u6veknn5frczsj4p2nq

Using GIS based algorithms for GCMs' performance evaluation

Salem S. Gharbia, Paul Johnston, Laurence Gill, Francesco Pilla
2016 2016 18th Mediterranean Electrotechnical Conference (MELECON)  
General circulation models (GCMs) are used for estimating future climate scenarios, run on a very coarse scale, so the outputs from GCMs need to be downscaled to obtain a finer spatial resolution. This paper provides a methodology for GCM-Ensembles performance evaluation using a GIS platform by applying statistical spatial downscaling methods. Statistical downscaling methods were used in the projection process after validation and performance evaluation using several techniques such as Taylor
more » ... agram for each GCM-ensembles within independent sub-periods. Climate change projections for the Shannon River catchment in Ireland were developed for temperature and precipitation from multi-GCM ensembles for three future time intervals forcing by different Representative Concentration Pathways (RCP). The changes in temperature and precipitation were spatially projected at a very fine spatial scale.
doi:10.1109/melcon.2016.7495476 fatcat:gpvyxooiofe3hix6dzmq3umpgm

Effect of COVID-19 on noise pollution change in Dublin, Ireland [article]

Bidroha Basu, Enda Murphy, Anna Molter, Arunima Sarkar Basu, Srikanta Sannigrahi, Miguel Belmonte, Francesco Pilla
2020 arXiv   pre-print
Noise pollution is considered to be the third most hazardous pollution after air and water pollution by the World Health Organization (WHO). Short as well as long-term exposure to noise pollution has several adverse effects on humans, ranging from psychiatric disorders such as anxiety and depression, hypertension, hormonal dysfunction, and blood pressure rise leading to cardiovascular disease. One of the major sources of noise pollution is road traffic. The WHO reports that around 40% of
more » ... s population are currently exposed to high noise levels. This study investigates noise pollution in Dublin, Ireland before and after the lockdown imposed as a result of the COVID-19 pandemic. The analysis was performed using 2020 hourly data from 12 noise monitoring stations. More than 80% of stations recorded high noise levels for more that 60% of the time before the lockdown in Dublin. However, a significant reduction in average and minimum noise levels was observed at all stations during the lockdown period and this can be attributed to reductions in both road and air traffic movements.
arXiv:2008.08993v1 fatcat:qbm554fhr5b2vkvpcvps5qr2tm

Tales of a city: Sentiment analysis of urban green space in Dublin

Mohammadhossein Ghahramani, Nadina J. Galle, Carlo Ratti, Francesco Pilla
2021 Cities  
Sentiment analysis (Ghahramani, Galle, Duarte, Ratti, & Pilla, 2021) can offer a way to make evidence-based improvements for urban green space.  ... 
doi:10.1016/j.cities.2021.103395 fatcat:3wn54lmi5zc6zptyfehzzpvoqm

Innovating with Nature: From Nature-Based Solutions to Nature-Based Enterprises

Esmee D. Kooijman, Siobhan McQuaid, Mary-Lee Rhodes, Marcus J. Collier, Francesco Pilla
2021 Sustainability  
Nature-based solutions (NBS) to address societal challenges have been widely recognised and adopted by governments in climate change and biodiversity strategies. Nevertheless, significant barriers exist for the necessary large-scale implementation of NBS and market development is still in its infancy. This study presents findings from a systematic review of literature and a survey on private sector agents in the planning and implementation of NBS, with the aim to identify them. In this study,
more » ... propose a typology for organisations delivering NBS and a categorisation of their economic activities. The most common organisation type found is nature-based enterprise which offers products or services where nature is a core element and used sustainably and engages in economic activity. Moreover, eleven categories of economic activities were identified, ranging from ecosystem restoration, living green roofs, and eco-tourism to smart technologies and community engagement for NBS. Nature-based enterprises contribute to a diverse range of sustainable economic activities, that standard industry classification systems do not adequately account for. The recognition of the value created by these activities is essential for designing effective policy support measures, and for market development of the sector and its potential to facilitate the wider adoption of NBS.
doi:10.3390/su13031263 fatcat:yy3mu2dterbf5gje32wvbwl4yy

Optimization and Machine Learning Applied to Last-Mile Logistics: A Review

Nadia Giuffrida, Jenny Fajardo-Calderin, Antonio D. Masegosa, Frank Werner, Margarete Steudter, Francesco Pilla
2022 Sustainability  
The growth in e-commerce that our society has faced in recent years is changing the view companies have on last-mile logistics, due to its increasing impact on the whole supply chain. New technologies are raising users' expectations with the need to develop customized delivery experiences; moreover, increasing pressure on supply chains has also created additional challenges for suppliers. At the same time, this phenomenon generates an increase in the impact on the liveability of our cities, due
more » ... to traffic congestion, the occupation of public spaces, and the environmental and acoustic pollution linked to urban logistics. In this context, the optimization of last-mile deliveries is an imperative not only for companies with parcels that need to be delivered in the urban areas, but also for public administrations that want to guarantee a good quality of life for citizens. In recent years, many scholars have focused on the study of logistics optimization techniques and, in particular, the last mile. In addition to traditional optimization techniques, linked to the disciplines of operations research, the recent advances in the use of sensors and IoT, and the consequent large amount of data that derives from it, are pushing towards a greater use of big data and analytics techniques—such as machine learning and artificial intelligence—which are also in this sector. Based on this premise, the aim of this work is to provide an overview of the most recent literature advances related to last-mile delivery optimization techniques; this is to be used as a baseline for scholars who intend to explore new approaches and techniques in the study of last-mile logistics optimization. A bibliometric analysis and a critical review were conducted in order to highlight the main studied problems, the algorithms used, and the case studies. The results from the analysis allow the studies to be clustered into traditional optimization models, machine learning approaches, and mixed methods. The main research gaps and limitations of the current literature are assessed in order to identify unaddressed challenges and provide research suggestions for future approaches.
doi:10.3390/su14095329 fatcat:xhzounvk4zgk5iwjsceggrbzkm

Regional Climate Impacts of Irrigation in Northern Italy Using a High Resolution Model

Arianna Valmassoi, Jimy Dudhia, Silvana Di Di Sabatino, Francesco Pilla
2020 Atmosphere  
Irrigation is crucial for sustaining agriculture in certain regions; however, there are effects on the local climate. Previous studies discussed that the irrigation signal might depend on the geographical region as well as the synoptic and climatic conditions. The work presented here aims to investigate the mechanisms behind changes in the irrigation impact on the local conditions depending on synoptic changes. Different to previous works, this employs convection-permitting simulations.
more » ... on processes are parameterized in three different ways depending on the evaporative loss. The region of focus is in northern Italy (Po Valley), which is of interest for both the soil-atmosphere coupling strength and widely used irrigation. The simulation period is Summer 2015 (May–July), which includes a heatwave month (July) and an average month (June). The results show how irrigation prevented the drying out of the soil layers during the heatwave. This influences the surface flux partition differently, by increasing moisture flux and decreasing the sensible heat flux. In general, the irrigation impact magnitude, with respect to the control simulation, is more than double in July compared to June. This study discusses climate implications for the region, such as the impact of widespread irrigation on the vegetation health, the heatwave feedback mechanism, atmospheric pollution, and human heat discomfort.
doi:10.3390/atmos11010072 fatcat:rbvi7t4h75bp3p3dnobzqd6y7m

Spatiotemporal effects of the causal factors on COVID-19 incidences in the contiguous United States [article]

Arabinda Maiti, Qi Zhang, Srikanta Sannigrahi, Suvamoy Pramanik, Suman Chakraborti, Francesco Pilla
2020 arXiv   pre-print
Sannigrahi, S., Pilla, F., Basu, B., Basu, A.S., Molter, A., 2020b.  ...  ., Pilla, F., Joshi, P. K., Basu, B., Keesstra, S., ... & Paul, S. K. (2020c).  ... 
arXiv:2010.15754v1 fatcat:7dcy6zkpung4zokpcph4qpabm4

Multi-GCM ensembles performance for climate projection on a GIS platform

Salem S. Gharbia, Laurence Gill, Paul Johnston, Francesco Pilla
2016 Modeling Earth Systems and Environment  
Climate impact studies especially in the field of hydrology often depend on climate change projections at fine spatial resolution. General circulation models (GCMs), which are the tools for estimating future climate scenarios, run on a very coarse scale, so the output from GCMs need to be downscaled to obtain a finer spatial resolution. This paper aims to present GIS platform as a downscaling environment through a suggested algorithm, which applies statistical downscaling models to
more » ... nal GCM-Ensembles simulations. Climate change projections for the Shannon River catchment in Ireland were developed for several climate variables from multi-GCM ensembles for three future time intervals forcing by different Representative Concentration Pathways (RCP): all these processes are implemented in a GIS platform through designed and developed GIS-based algorithm. This algorithm is used as a downscaling tool in GIS environment, which is unprecedented in literature. Statistical downscaling methods were used in the projection process after a particular verification and performance evaluation using several techniques such as Taylor diagram for each GCM-ensembles within independent sub-periods. The established statistical relationships were used to predict the response of the future climate from simulated climate model changes of the coarse scale variables. Significant changes in temperature, precipitation, wind speed, solar radiation and relative humidity were projected at a very fine spatial scale. It was concluded that the main source of uncertainty was related to the GCMs simulation and selection. In addition, it was obvious to conclude that GIS platform is an efficient tool for spatial downscaling using raster data forms.
doi:10.1007/s40808-016-0154-2 fatcat:sv3xsbzvbzeaxl4xnhkvmozgty

Urban Water Flow and Water Level Prediction Based on Deep Learning [chapter]

Haytham Assem, Salem Ghariba, Gabor Makrai, Paul Johnston, Laurence Gill, Francesco Pilla
2017 Lecture Notes in Computer Science  
., Pilla, F.: Multi-gcm ensembles performance for climate projection on a gis platform. Modeling Earth Systems and Environment ( ), -( ) .  ...  ., Pilla, F.: Land use scenarios and projections simulation using an integrated gis cellular automata algorithms. Modeling Earth Systems and Environment ( ), ( ) .  ... 
doi:10.1007/978-3-319-71273-4_26 fatcat:igrq2hcvwnglxoiftqcytstzju

Effects of West Coast forest fire emissions on atmospheric environment: A coupled satellite and ground-based assessment [article]

Srikanta Sannigrahi, Qi Zhang, Francesco Pilla, Bidroha Basu, Arunima Sarkar Basu
2020 arXiv   pre-print
Forest fires have a profound impact on the atmospheric environment and air quality across the ecosystems. The recent west coast forest fire in the United States of America (USA) has broken all the past records and caused severe environmental and public health burdens. As of middle September, nearly 6 million acres forest area were burned, and more than 25 casualties were reported so far. In this study, both satellite and in-situ air pollution data were utilized to examine the effects of this
more » ... recedented wildfire on the atmospheric environment. The spatiotemporal concentrations of total six air pollutants, i.e. carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3), particulate matter (PM2.5 and PM10), and aerosol index (AI), were measured for the periods of 15 August to 15 September for 2020 (fire year) and 2019 (reference year). The in-situ data-led measurements show that the highest increases in CO (ppm), PM2.5, and PM10 concentrations (μg/m3) were clustered around the west coastal fire-prone states, during the 15 August - 15 September period. The average CO concentration (ppm) was increased most significantly in Oregon (1147.10), followed by Washington (812.76), and California (13.17). Meanwhile, the concentration (μg/m3) in particulate matter (both PM2.5 and PM10), was increased in all three states affected severely by wildfires. Changes (positive) in both PM2.5 and PM10 were measured highest in Washington (45.83 and 88.47 for PM2.5 and PM10), followed by Oregon (41.99 and 62.75 for PM2.5 and PM10), and California (31.27 and 35.04 for PM2.5 and PM10). The average level of exposure to CO, PM2.5, and PM10 was also measured for all the three fire-prone states. The results of the exposure assessment revealed a strong tradeoff association between wildland fire and local/regional air quality standard.
arXiv:2010.12977v1 fatcat:6ozhsarjhbdz3deg3zmfww4e3m
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